unit08-large-language-models/8.2-bag-of-words/8.2-part2-bag-of-words-classifier.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# 8.2 Training a Bag-of-words Model\n",
    "\n",
    "## Part 2: Training a Logistic Regression Classifier"
   ]
  },
  {
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   "execution_count": 1,
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     "text": [
      "torch       : 1.13.1\n",
      "lightning   : 1.9.2\n",
      "pandas      : 1.5.3\n",
      "scikit-learn: 1.2.2\n",
      "\n",
      "conda environment: dl-fundamentals\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -p torch,lightning,pandas,scikit-learn --conda"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7bcc3a9-fea0-4039-b284-5a4012f1ce47",
   "metadata": {},
   "source": [
    "# 1 Loading the dataset into DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e39e2228-5f0b-4fb9-b762-df26c2052b45",
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   "source": [
    "# pip install datasets\n",
    "\n",
    "import os.path as op\n",
    "\n",
    "import lightning as L\n",
    "from lightning.pytorch.loggers import CSVLogger\n",
    "from lightning.pytorch.callbacks import ModelCheckpoint\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import torch\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "from local_dataset_utilities import download_dataset, load_dataset_into_to_dataframe, partition_dataset\n",
    "from local_dataset_utilities import IMDBDataset\n",
    "\n",
    "from local_utilities import LightningModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fb31ac90-9e3a-41d0-baf1-8e613043924b",
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   "outputs": [
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     "text": [
      "100%|███████████████████████████████████| 50000/50000 [00:20<00:00, 2393.00it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class distribution:\n"
     ]
    }
   ],
   "source": [
    "download_dataset()\n",
    "\n",
    "df = load_dataset_into_to_dataframe()\n",
    "partition_dataset(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "221f30a1-b433-4304-a18d-8d03abd42b58",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv(\"train.csv\")\n",
    "df_val = pd.read_csv(\"val.csv\")\n",
    "df_test = pd.read_csv(\"test.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3418c5a2-f9d8-4e43-a21f-24a5f28ea407",
   "metadata": {},
   "source": [
    "## 2) Bag-of-Words Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a423f671-954b-4ced-b10c-c9c5cbff1ac0",
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = CountVectorizer(lowercase=True, max_features=10_000, stop_words=\"english\")\n",
    "\n",
    "cv.fit(df_train[\"text\"])\n",
    "\n",
    "X_train = cv.transform(df_train[\"text\"])\n",
    "X_val = cv.transform(df_val[\"text\"])\n",
    "X_test = cv.transform(df_test[\"text\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c802bc3a-305f-434a-895e-3a8d640a971b",
   "metadata": {},
   "source": [
    "## 3) Defining the DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "46feb4f3-2080-4755-a3d5-75661c1a8729",
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   "source": [
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "\n",
    "class TextDataset(Dataset):\n",
    "    def __init__(self, X, y):\n",
    "\n",
    "        self.features = torch.tensor(X, dtype=torch.float32)\n",
    "        self.labels = torch.tensor(y, dtype=torch.int64)\n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        x = self.features[index]\n",
    "        y = self.labels[index]        \n",
    "        return x, y\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.labels.shape[0]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c248c4f6-3727-47e6-b126-10f50e8697c4",
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   "source": [
    "train_ds = TextDataset(X_train.todense(), df_train[\"label\"].values)\n",
    "\n",
    "train_loader = DataLoader(\n",
    "    dataset=train_ds,\n",
    "    batch_size=32,\n",
    "    shuffle=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "715ac05d-b00e-4512-a3c0-f53185692de0",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_ds = TextDataset(X_val.todense(), df_val[\"label\"].values)\n",
    "\n",
    "val_loader = DataLoader(\n",
    "    dataset=val_ds,\n",
    "    batch_size=32,\n",
    "    shuffle=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9123f592-25b2-436a-8a21-bacd6d963464",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_ds = TextDataset(X_test.todense(), df_test[\"label\"].values)\n",
    "\n",
    "test_loader = DataLoader(\n",
    "    dataset=test_ds,\n",
    "    batch_size=32,\n",
    "    shuffle=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6d080edb-388a-42a5-a1c3-6f93c137b24a",
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   "outputs": [],
   "source": [
    "for batch_idx, (features, class_labels) in enumerate(train_loader):\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3759d7a1-ac30-4323-a9ca-0b990bb90e0e",
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   "outputs": [
    {
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      "text/plain": [
       "torch.Size([32, 10000])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9168e8c1-c870-4762-a34d-f02e4b6a11c2",
   "metadata": {},
   "source": [
    "## 4) Implementing the Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "25732e45-b82d-4103-ac6b-f04f11a268b1",
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   "outputs": [],
   "source": [
    "class LogisticRegression(torch.nn.Module):\n",
    "    \n",
    "    def __init__(self, num_features, num_classes):\n",
    "        super().__init__()\n",
    "        self.linear = torch.nn.Linear(num_features, num_classes)\n",
    "    \n",
    "    def forward(self, x):\n",
    "        logits = self.linear(x)\n",
    "        return logits\n",
    "    \n",
    "    \n",
    "pytorch_model = LogisticRegression(num_features=10_000, num_classes=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4f66c8e3-0052-4e07-a15c-f837f2876787",
   "metadata": {},
   "outputs": [],
   "source": [
    "callbacks = [\n",
    "    ModelCheckpoint(save_top_k=1, mode=\"max\", monitor=\"val_acc\", save_last=True)\n",
    "]"
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   "execution_count": 14,
   "id": "d14d4685-4426-4345-8386-9d20bcd6fb2a",
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      "GPU available: True (mps), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "/Users/sebastian/miniforge3/envs/dl-fundamentals/lib/python3.10/site-packages/lightning/pytorch/trainer/setup.py:201: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n",
      "  rank_zero_warn(\n",
      "\n",
      "  | Name      | Type               | Params\n",
      "-------------------------------------------------\n",
      "0 | model     | LogisticRegression | 20.0 K\n",
      "1 | train_acc | MulticlassAccuracy | 0     \n",
      "2 | val_acc   | MulticlassAccuracy | 0     \n",
      "3 | test_acc  | MulticlassAccuracy | 0     \n",
      "-------------------------------------------------\n",
      "20.0 K    Trainable params\n",
      "0         Non-trainable params\n",
      "20.0 K    Total params\n",
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      "/Users/sebastian/miniforge3/envs/dl-fundamentals/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:488: PossibleUserWarning: Your `val_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test/predict dataloaders.\n",
      "  rank_zero_warn(\n",
      "/Users/sebastian/miniforge3/envs/dl-fundamentals/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:224: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n",
      "/Users/sebastian/miniforge3/envs/dl-fundamentals/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:224: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
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   ],
   "source": [
    "lightning_model = LightningModel(model=pytorch_model, learning_rate=0.05)\n",
    "\n",
    "trainer = L.Trainer(\n",
    "    callbacks=callbacks,\n",
    "    max_epochs=30,\n",
    "    accelerator=\"cpu\",\n",
    "    logger=CSVLogger(save_dir=\"logs/\", name=\"my-model\"),\n",
    "    deterministic=True,\n",
    ")\n",
    "\n",
    "trainer.fit(model=lightning_model, train_dataloaders=train_loader, val_dataloaders=val_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "62f8e23e-50b1-4940-8ba3-02e2321b0b9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from local_utilities import plot_csv_logger\n",
    "\n",
    "plot_csv_logger(csv_path=f\"{trainer.logger.log_dir}/metrics.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d9f988f5-5cd7-43ed-abef-a893fb4fcef6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sebastian/miniforge3/envs/dl-fundamentals/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:488: PossibleUserWarning: Your `test_dataloader`'s sampler has shuffling enabled, it is strongly recommended that you turn shuffling off for val/test/predict dataloaders.\n",
      "  rank_zero_warn(\n",
      "/Users/sebastian/miniforge3/envs/dl-fundamentals/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:224: PossibleUserWarning: The dataloader, test_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 8 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ff3da32bf401438eb9e34b29f8805716",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8827000260353088     </span>│\n",
       "└───────────────────────────┴───────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[36m \u001b[0m\u001b[36m        test_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8827000260353088    \u001b[0m\u001b[35m \u001b[0m│\n",
       "└───────────────────────────┴───────────────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[{'test_acc': 0.8827000260353088}]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer.test(model=lightning_model, dataloaders=test_loader)"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
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